TempDisaggDGP {DisaggregateTS} | R Documentation |
High and Low-Frequency Data Generating Processes
Description
This function generates the high-frequency n \times 1
response vector y
, according to y=X\beta+\epsilon
, where X
is an n\times p
matrix of indicator
series, and the p\times 1
coefficient vector may be sparse. The low-frequency n_l\times 1
vector Y
can be generated by pre-multiplying an aggregation matrix n_l\times n
matrix, such that the sum, the average, the last or the first value of y
equates the
corresponding Y
observation. The parameter aggRatio is the specified aggregation ratio between the low and high frequency series, e.g. aggRatio = 4 for annual-to-quarterly
and aggRatio = 3 for quarterly-to-monthly. If n > aggRatio \times n_l
, then the last n - aggRatio \times n_l
columns of the aggregation matrix are 0 such that
Y
is only observed up to n_l
.
For a comprehensive review, see Dagum and Cholette (2006).
Usage
TempDisaggDGP(
n_l,
n,
aggRatio = 4,
p = 1,
beta = 1,
sparsity = 1,
method = "Chow-Lin",
aggMat = "sum",
rho = 0,
mean_X = 0,
sd_X = 1,
sd_e = 1,
simul = FALSE,
setSeed = 42
)
Arguments
n_l |
Size of the low frequency series. |
n |
Size of the high frequency series. |
aggRatio |
aggregation ratio (default is 4) |
p |
The number of high-frequency indicator series to include. |
beta |
The positive and negative beta elements for the coefficient vector. |
sparsity |
Sparsity percentage of the coefficient vector. |
method |
DGP of residuals, either 'Denton', 'Denton-Cholette', 'Chow-Lin', 'Fernandez', 'Litterman'. |
aggMat |
Aggregation matrix according to 'first', 'sum', 'average', 'last'. |
rho |
The residual autocorrelation coefficient. Default is 0. |
mean_X |
Mean of the design matrix. Default is 0. |
sd_X |
Standard deviation of the design matrix. Default is 1. |
sd_e |
Standard deviation of the errors. Default is 1. |
simul |
When 'TRUE' the design matrix and the coefficient vector are fixed. |
setSeed |
The seed used when 'simul' is set to 'TRUE'. |
Value
y_Gen Generated high-frequency response series.
Y_Gen Generated low-frequency response series.
X_Gen Generated high-frequency indicator series.
Beta_Gen Generated coefficient vector.
e_Gen Generated high-frequency residual series.
References
Dagum EB, Cholette PA (2006). Benchmarking, temporal distribution, and reconciliation methods for time series, volume 186. Springer Science \& Business Media.
Examples
data = TempDisaggDGP(n_l=25, n=100, aggRatio=4,p=10, rho=0.5)
X = data$X_Gen
Y = data$Y_Gen